• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.375-381
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• 2023

In an increasingly hyperconnected battlefield, the reliance on battlefield networks and AI-based autonomous weapons systems creates uncertainty and raises ethical concerns. This article explores the responsibility gap in operating autonomous weapons systems, using Robert Sparrow's theory. By analyzing Sparrow's arguments, we propose overcoming the responsibility gap in lethal autonomous weapon systems (LAWS). Our objective is to establish a framework of responsibility that aligns with the evolving battlefield, promoting the development and use of responsible weapon systems.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.845-850
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• 2023

In this paper, we propose a method for diagnosing ball bearing vibration using transfer learning. STFT, which can analyze vibration signals in time-frequency, was used as input to CNN to diagnose failures. In order to rapidly learn CNN-based deep artificial neural networks and improve diagnostic performance, we proposed a transfer learning-based deep learning learning technique. For transfer learning, the feature extractor and classifier were selectively learned using a VGG-based image classification model, the data set for learning was publicly available ball bearing vibration data provided by Case Western Reserve University, and performance was evaluated by comparing the proposed method with the existing CNN model. Experimental results not only prove that transfer learning is useful for condition diagnosis in ball bearing vibration data, but also allow other industries to use transfer learning to improve condition diagnosis.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.707-714
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• 2023

The Internet of Things, which is the key factor of the 4th industrial revolution, are apt to apply to many systems. The existing security mechanism cannot be realized with limited resources such as low capacity of devices and sensors. In order to apply IoT system, a new structure and ultra-lightweight encryption is required. In this paper, we analyzed security issues that can operate in Internet-based smart home networks, and to solve the critical issues against these attacks, technologies for device protection between heterogeneous devices. Security requirements are required to protect from attacks. Therefore, we analyzed the demands and requirements for its application by analyzing the security architecture and features in smart home network.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.24-31
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• 2017

As the concept of sustainability becomes more and more popular, a large amount of literature have been recorded recently on intelligent green building and smart city (IGB&SC). It is therefore needed to systematically analyse the existing knowledge structure as well as the future new development of this domain through the identification of the thematic trends, landmark articles, typical keywords together with co-operative researchers. In this paper, Citespace software package is applied to analyse the citation networks and other relevant data of the past eleven years (from 2006 to 2016) collected from Web of Science (WOS). Through this, a series of professional document analysis are conducted, including the production of core authors, the influence made by the most cited authors, keywords extraction and timezone analysis, hot topics of research, highly cited papers and trends with regard to co-citation analysis, etc. As a result, the development track of the IGB&SC domains is revealed and visualized and the following results reached: (i) in the research area of IGB&SC, the most productive researcher is Winters JV and Caragliu A is most influential on the other hand; (ii) different focuses of IGB&SC research have been emerged continually from 2006 to 2016 e.g. smart growth, sustainability, smart city, big data, etc.; (iii) Hollands's work is identified with the most citations and the emerging trends, as revealed from the bursts analysis in document co-citations, can be concluded as smart growth, the assessment of intelligent green building and smart city.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• Korea Trade Review
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• v.48 no.3
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• pp.243-262
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• 2023

This study provides a systematic review of smart trade contracts, examining the research trends and theoretical background of utilizing smart contracts and blockchain technology for the digitalization and automation of trade contracts. Smart trade contracts are a concept that applies the automated contract system based on blockchain to trade-related transactions. The study analyzes the technical and legal challenges and proposes solutions. The technical aspect covers the development of smart contract platforms, scalability and performance improvements of blockchain networks, and security and privacy concerns. The legal aspect addresses the legal enforceability of smart contracts, automatic execution of contract conditions, and the responsibilities and obligations of contract parties. Smart trade contracts have been found to have applications in various industries such as international trade, supply chain management, finance, insurance, and energy, contributing to the ease of trade finance, efficiency of supply chains, and business model innovation. However, challenges remain in terms of legal regulations, interaction with existing legal frameworks, and technological aspects. Further research is needed, including empirical studies, business model innovation, resolution of legal issues, security and privacy considerations, standardization and collaboration, and user experience studies to address these challenges and explore additional aspects of smart trade contracts.

• Imaging Science in Dentistry
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• v.53 no.4
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• pp.271-281
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• 2023

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved through classification, detection, and segmentation tasks. Materials and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases. Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentation tasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion: CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs. Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.10-21
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• 2024

Recently, audio systems are changing the configuration of conventional sound reinforcement (SR) systems and public address (PA) systems by using audio over IP (AoIP), a technology that can transmit and receive audio signals based on internet protocol (IP). With the advancement of IP technology, AoIP technologies are leading the audio market and various technologies are being released. In particular, audio networks and control hierarchy over peer-to-peer (Anchor) technology based on AoIP is a system that transmits and receives audio signals over a wide bandwidth without an audio mixer, creating a novel paradigm for existing audio system configurations. Anchor technology forms an audio system by connecting audio sources and output equipment with On-site audio center (OAC), a device that can transmit and receive IP. Anchor's receiving OAC is capable of receiving and mixing audio signals transmitted from different IPs, making it possible to configure a novel audio system by replacing the conventional audio mixer. However, Anchor technology does not have the ability to provide audio effects to input devices such as microphones and instruments in the audio system configuration. Due to this, when individual control of each audio source is required, there is a problem of not being able to control the input signal, and it is impossible to individually affect a specific input signal. In this paper, we implemented a tone control module that can individually control the tone of the audio source of the input device using the audio processor core in the audio system based on Anchor technology, tone control for audio sources is possible through a tone control module connected to the transmitting OAC. As a result of the study, we confirmed that OAC receives the signal from the audio source, adjusts the tone and outputs it on the tone control module. Based on this, it was possible to solve problems that occurred in Anchor technology through transmitting OAC and tone control modules. In the future, we hope that the audio system configuration using Anchor technology will become established as the standard for audio equipment.

• Animal Bioscience
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• v.37 no.4_spc
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• pp.775-785
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• 2024

Pork production is a significant agricultural enterprise in the United States and Canada. The United States is the third-largest global producer of pork and Canada ranks seventh in pork production. The North American Free Trade Agreement and its successor, the U.S.-Mexico-Canada Agreement, have facilitated trade and integration between the two countries. The majority of production systems are modern and intensive, characterized by large vertically integrated farms using advanced technologies. Both nations benefit from their status as major producers of feed grains, with the United States leading in corn and soybeans, while Canada excels in canola and barley production. The regulatory frameworks for food safety, animal welfare, and environmental stewardship differ slightly, with the FDA and USDA overseeing these aspects in the United States, and Health Canada and the Canada Food Inspection Agency in Canada. The United States and Canada also have well-established distribution networks for pork products, relying on both domestic and international markets. Export markets play a crucial role, with the United States being a major importer of Canadian pigs, and both countries exploring opportunities in Asia. Despite a rise in global demand, domestic pork consumption trends differ, with per capita consumption remaining stable in the USA and declining in Canada. Changing consumer preferences, including a demand for ethically raised and locally sourced pork, may influence production practices. Future trends in pig production include a focus on consumer concerns, sustainability, disease prevention, reduction of antimicrobial use, and advancements in technology. The industry is adapting to challenges such as disease outbreaks and changing regulations, with a strong emphasis on animal welfare. Labor and workforce considerations, along with advancements in technology and automation, are expected to shape the efficiency of pork production in the future.